A common theme that\u2019s been explored in this column for some time is the idea that, at the current state of the art, machine intelligence is clearly inferior to human intelligence, to the point that the term machine intelligence should perhaps be regarded as an oxymoron.\u00a0 That isn\u2019t to say that an actual thinking or sentient machine wouldn\u2019t be welcome, or that it should be greeted with fear and shunned as an abomination.\u00a0 Rather it is based on a cold-eye, unemotional assessment of where we stand and of the huge gap in time and technology that separates us from the apocalyptic stories as featured in the Terminator or in Demon Seed.<\/p>\n
As a case in point that illustrates this gap, consider the evolution of machine automation.<\/p>\n
The concept of machine automation is nothing new and it dates as far back as man has made machines.\u00a0 But the perceived threat of machine automation as being harmful to mankind seems to have its genesis shortly after the beginning of the Industrial Revolution<\/a>.<\/p>\n While historians don\u2019t agree on exactly when and how the Industrial Revolution began, it is clear that it got its start in Great Britain sometime around 1780, and eventually completed its spread to most of the Western World by 1840.\u00a0 During that time, machines of all varieties were invented to perform activities that were originally performed by hand.\u00a0 Of course there was a backlash by certain segments of society, and perhaps the most notable was by the Luddites in England.<\/p>\n The Luddites were a group of textile artisans who felt threatened by the invention of knitting, spinning, and weaving machines that made textile manufacturing accessible by lower-skill workers. During the period from about 1811 to 1817, they were known for destroying factory machinery and protesting the encroachment of machines into their economic sphere.\u00a0 And while it is true that these machines probably weakened textile artisan position in society, nowhere do we hear a claim that the machine dislodged the clothing designer.\u00a0 Nor do we hear that the designers of the machine didn\u2019t know how to weave, spin or knit.\u00a0 What we hear is simply that the machines did the same job as the human, faster and with fewer errors (and of course, ultimately cheaper) using the techniques developed by human beings.<\/p>\n The idea of machine automation as a threat has waxed and waned over the years.\u00a0 Another example can be found starting in the late 1950s, where workers in office settings felt threatened by the rise of the computer as a business machine.\u00a0 The delightful movie Desk Set, starring Spencer Tracey and Katherine Hepburn, is a comical romp through the fears and realities of machines displacing human beings through automation.\u00a0 Tracey plays the role of Richard Sumner, who is a \u2018Methods Engineer\u2019 (computer scientist) that has been hired to provide a computer for the research department at a major television network in New York in advance of a corporate merger.\u00a0 Katherine Hepburn plays the role of Bunny Watson (I\u2019m not making that name up), who is the head of the research department, and whose entire contingent is filled with smart, witty, and attractive women.\u00a0 Comic hijinks ensue and Sumner and Watson end up falling in love, but there is a very level-headed message that is given in the movie towards the end, when Sumner explains that the purpose of his computer is to store and retrieve data so as to free the women for tasks best suited for a human being since, as he likes to say, \u2018no machine can evaluate.\u2019<\/p>\n Fast forward to today.\u00a0 We don\u2019t just have machines that weave fabric or collate research data.\u00a0 We have machines that act as AI players in video games; that automate complex robotic manufacturing; that print 3-D objects; that control computer updates and traffic lights and billing notices and hundreds of other things.\u00a0 But they only do what we ourselves have taught them to do.<\/p>\n Nowhere is there a record of a single machine inventing a new process, designing a new object, or developing a new idea.\u00a0 True, they assist us in all of these tasks, but they do so using the well-defined methodologies that we taught them.\u00a0 True that they allow us to comb through vast amounts of data and gain deeper insight than we could have gotten by going through the data by hand.\u00a0 But the patterns they search for and the insights they help bring to light are fundamentally what we put in.<\/p>\n In other words, they do what we tell them to do precisely how we told them to do it.\u00a0 That we are sometimes surprised by their results shouldn\u2019t come as a shock.\u00a0 It is well known that any set of rules of reasonable complexity, which are seemingly understandable and sensible on their own, can sometimes produce unforeseen results when they interact.\u00a0 Ask any person harmed by the unintended consequence of a law written by people, administered by people, and acting on people.\u00a0 This doesn\u2019t mean that the law itself somehow became intelligent or achieved sentience, but rather we were too busy or too rushed or simply too stupid to think it all the way through.<\/p>\n